In common urine sediment analysis, first, a urine sample image is shot using a microscope imaging system. Then, the candidate blocks in the urine sample image are segmented using, for example, an edge detection technology. By removing obvious background blocks from these candidate blocks, blocks to be processed are detected. Next, the blocks to be processed are processed.
Currently, there are mainly two directions of processing the blocks to be processed. The first direction is classification, i.e. directly classifying these blocks to be processed into various visible element (such as a cast, an epithelium and an erythrocyte) blocks and background blocks that are easily confused with visible elements. The other direction is block retrieval, which does not directly classify the blocks to be processed but retrieves blocks similar to the previously stored blocks to be processed in a database. The unique difference with regard to the result of classification lies in that block retrieval may retrieve a plurality of similar blocks to be provided to a user, and thus can provide more information for the user. The user may perform a further selection or judgement in the plurality of similar blocks.
Currently, classification and block retrieval achieved by a machine automatically generally use an approach of machine learning. Several features for classification or block retrieval are specified and constitute a feature set. A large number of training sample blocks are firstly used to constitute a training sample set for training a processing model (a classification model or a block retrieval model). With regard to each training sample block in the training sample set, the features in the feature set are calculated for the processing model to learn. In this way, when the trained processing model receives a new block to be processed, the features in the feature set are calculated for the new block to be processed, and according to the calculated features in the feature set and with reference to the previous learning result, classification can be performed thereon or the previously stored similar images are retrieved therefor.